The following relates to the magnetic resonance arts. It particularly relates to the imaging, tracking, and displaying of neural fibers and fiber bundles by diffusion tensor magnetic resonance imaging (DT-MRI), and will be described with particular reference thereto. However, it will also find application in conjunction with the tracking and graphical rendering of other types of fibrous structures as well as with other imaging modalities such as single photon emission computed tomography imaging (SPECT), computed tomography (CT), positron emission tomography (PET), and also in crystallography and geophysics.
Nerve tissue in human beings and other mammals includes neurons with elongated axonal portions arranged to form neural fibers or fiber bundles, along which electrochemical signals are transmitted. In the brain, for example, functional areas defined by very high neural densities are typically linked by structurally complex neural networks of axonal fiber bundles. The axonal fiber bundles and other fibrous material are substantially surrounded by other tissue.
Diagnosis of neural diseases, planning for brain surgery, and other neurologically related clinical activities as well as research studies on brain functioning may benefit from non-invasive imaging and tracking of the axonal fibers and fiber bundles. In particular, diffusion tensor magnetic resonance imaging (DT-MRI) has been shown to provide image contrast that correlates with axonal fiber bundles. In the DT-MRI technique, diffusion-sensitizing magnetic field gradients are applied in the excitation/imaging sequence so that the magnetic resonance images include contrast related to the diffusion of water or other fluid molecules. By applying the diffusion gradients in selected directions during the excitation/imaging sequence, diffusion weighted images are acquired from which diffusion tensor coefficients are obtained for each voxel location in image space.
The intensity of individual voxels are fitted to calculate six independent variables in a 3×3 diffusion tensor. The diffusion tensor is then diagonalized to obtain three eigenvalues and corresponding three eigenvectors. The eigenvectors represent the local direction of the brain fiber structure at the voxel at issue. Within an imaging voxel, the directional information of white matter tracts is usually obtained from the major eigenvector of the diffusion tensor.
Using the local directional information, a global anatomical connectivity of white matter tracts is constructed. Each time the tracking connects a pixel to the next pixel, judgment is made whether the fiber is continuous or terminated based on randomness of the fiber orientation of the adjacent pixels. The interplay between the local directional and global structural information is crucial in understanding changes in white matter tracts. However, image noise may produce errors in the calculated tensor, and, hence, in its eigenvalues and eigenvectors. There is an uncertainty associated with every estimate of fiber orientation. Accumulated uncertainties in fiber orientation may lead to erroneous reconstruction of pathways. It is difficult to trace reliably in the uncertain areas.
It is desirable to determine and display the tract dispersion, e.g., the eigenvectors and the associated uncertainties. For example, the unit eigenvector may be displayed with a cone of uncertainty around its tip. This conveys the notion that the direction of fiber is not known precisely.
However, the methods known in the art are directed to computation and visualization of a circular cone of uncertainty. These methods are not suitable for practical computation and visualization of an elliptical cone of uncertainty.
There is a need for the apparatuses and methods to overcome the above-referenced problems and others.